Content identifiers triggering corresponding responses through collaborative processing

ABSTRACT

Fingerprint data derived from audio or other content is used as an identifier. The fingerprint data can be derived from the content. In one embodiment, fingerprint data supplied from two or more sources is aggregated. The aggregated fingerprint data is used to define a set of audio signals. An audio signal from the set of audio signals is selected based on its probability of matching the fingerprint data. Digital watermarks can also be similarly used to define a set of audio signals.

RELATED APPLICATION DATA

This application is a continuation-in-part of application Ser. No.09/858,189, filed May 14, 2001, which is a continuation in part ofapplication Ser. No. 09/571,422, filed May 15, 2000 now U.S. Pat. No.6,947,571. Application Ser. No. 09/571,422 claims priority benefit toeach of the following provisional applications: Ser. No. 60/141,468,filed Jun. 29, 1999; Ser. No. 60/151,586, filed Aug. 30, 1999; Ser. No60/158,015, filed Oct. 6, 1999; Ser. No. 60/163,332, filed Nov. 3, 1999;and Ser. No. 60/164,619, filed Nov. 10, 1999. Application Ser. No.09/571,422 is also a continuation-in-part of each of the followingutility applications: Ser. No. 09/314,648, filed May 19, 1999 now U.S.Pat. No. 6,681,028; Ser. No. 09/342,688, filed Jun. 29, 1999 now U.S.Pat. No. 6,650,761; Ser. No. 09/342,689, filed Jun. 29, 1999 now U.S.Pat. No. 6,311,214; Ser. No. 09/342,971, filed Jun. 29, 1999 nowabandoned; Ser. No. 09/343,101, filed Jun. 29, 1999 now abandoned; Ser.No. 09/343,104, filed Jun. 29, 1999 now abandoned; Ser. No. 09/531,076,filed Mar. 18, 2000; Ser. No. 09/543,125, filed Apr. 5, 2000; Ser. No.09/547,664, filed Apr. 12, 2000; and Ser. No. 09/552,998, filed Apr. 19,2000 now abandoned.

This application is also a continuation-in-part of copending applicationSer. Nos. 09/574,726 and 09/476,686, both of which claim priority toapplication Ser. No. 60/134,782.

The present application claims priority benefit to the foregoingapplications.

The subject matter of this application is also related to that of Ser.Nos. 09/620,019, 60/257,822, 60/232,163, and 09/404,291.

FIELD OF THE INVENTION

The present invention relates to computer-based systems, and moreparticularly relates to systems that identify electronic or physicalobjects (e.g., audio, printed documents, video, etc.), and triggercorresponding responses.

BACKGROUND

In application Ser. No. 09/571,422 (now laid-open as PCT publication WO00/70585), the present assignee described technology that can sense anobject identifier from a physical or electronic object, and trigger acorresponding computer response.

In applications Ser. Nos. 09/574,726 and 09/476,686, the presentassignee described technology that uses a microphone to sense audiosounds, determine an identifier corresponding to the audio, and thentrigger a corresponding response.

DETAILED DESCRIPTION

Although the cited patent applications focused on use of digitalwatermarks to identify the subject objects/audio, they noted that thesame applications and benefits can be provided with other identificationtechnologies.

One such suitable technology—variously known as robust hashing,fingerprinting, etc.—involves generating an identifier from attributesof the content. This identifier can then be looked-up in a database (orother data structure) to determine the song (or other audio track) towhich it corresponds.

Various fingerprinting technologies are known. For example, a softwareprogram called TRM, from Relatable Software, was written up in theWashington Post as follows:

-   -   TRM performs a small technological miracle: It “fingerprints”        songs, analyzing beat and tempo to generate a unique digital        identifier. Since every song is slightly different, no two        “acoustic fingerprints” are alike, not even live and studio        versions of the same melody.

Tuneprint is another such audio fingerprinting tool. Tuneprint isunderstood to utilize a model of human hearing used to predict how audiowill appear after it's been distorted by the human ear, and the parts ofneural processing that are understood. This is some of the sameinformation that led to MP3 encoders achieving exceptional audiocompression. Characteristics that uniquely identify the track are thenidentified by picking out the most important, surprising, or significantfeatures of the sound.

Yet another fingerprinting program is Songprint, available as an opensource library from freetantrum.org.

Still other fingerprinting technologies are available from Cantametrix(see, e.g., published patent applications WO01/20483 and WO01/20609).

One particular approach to fingerprinting is detailed in the presentassignee's application Ser. No. 60/263,490, filed Jan. 22, 2001.

One form of fingerprint may be derived by applying content—in whole orpart, and represented in time- or frequency format—to a neural network,such as a Kohonen self-organizing map. For example, a song may beidentified by feeding the first 30 seconds of audio, with 20 millisecondFourier transformed windows, into a Kohonen network having 64 outputs.The 64 outputs can, themselves, form the fingerprint, or they can befurther processed to yield the fingerprint.

A variety of other fingerprinting tools and techniques are known toartisans in the field. Others are disclosed, e.g., in applications Ser.Nos. 60/257,822, 09/563,664, and 09/578,551. See also the chapter onFingerprinting by John Hyeon Lee, in Information Hiding: Techniques forStenography and Digital Watermarking edited by Stefan Katzenbeisse andFabien A. P. Petitcolas, published by Artech House.

One way to generate a fingerprint is to “hash” the audio, to derive ashorter code that is dependent, in a predetermined way, on the audiodata. However, slight differences in the audio data (such as samplingrate) can cause two versions of the same song to yield two differenthash codes. While this outcome is advantageous in certain outcomes, itis disadvantageous in many others.

Generally preferable are audio fingerprinting techniques that yield thesame fingerprints, even if the audio data are slightly different. Thus,a song sampled at a 96K bit rate desirably should yield the samefingerprint as the same song sampled at 128K. Likewise, a song embeddedwith steganographic watermark data should generally yield the samefingerprint as the same song without embedded watermark data.

One way to do this is to employ a hash function that is insensitive tocertain changes in the input data. Thus, two audio tracks that areacoustically similar will hash to the same code, notwithstanding thefact that individual bits are different. A variety of such hashingtechniques are known.

Another approach does not rely on “hashing” of the audio data bits.Instead, the audio is decomposed into elements having greater or lesserperceptibility. Audio compression techniques employ such decompositionmethods, and discard the elements that are essentially imperceptible. Infingerprinting, these elements can also be disregarded, and the“fingerprint” taken from the acoustically significant portions of theaudio (e.g., the most significant coefficients after transformation ofthe audio into a transform domain, such as DCT).

Some fingerprinting techniques do not rely on the absolute audio data(or transformed data) per se, but rather rely on the changes in suchdata from sample to sample (or coefficient to coefficient) as anidentifying hallmark of the audio.

Some fingerprinting algorithms consider the entire audio track (e.g., 3minutes). Others work on much shorter windows—a few seconds, orfractions of seconds. The former technique yields a single fingerprintfor the track. The latter yields plural fingerprints—one from eachexcerpt. (The latter fingerprints can be concatenated, or otherwisecombined, to yield a master fingerprint for the entire audio track.) Forcompressed audio, one convenient unit from which excerpts can be formedis the frame or window used in the compression algorithm (e.g., theexcerpt can be one frame, five frames, etc.).

One advantage to the excerpt-based techniques is that a song can becorrectly identified even if it is truncated. Moreover, the technique iswell suited for use with streaming media (in which the entire song datais typically not available all at once as a single file).

In database look-up systems employing fingerprints from short excerpts,a first fingerprint may be found to match 10 songs. To resolve thisambiguity, subsequent excerpt-fingerprints can be checked.

One way of making fingerprints “robust” against variations among similartracks is to employ probabilistic methods using excerpt-basedfingerprints. Consider the following, over-simplified, example:

Fingerprinted excerpt Matches these songs in database Fingerprint 1 A,B, C Fingerprint 2 C, D, E Fingerprint 3 B, D, F Fingerprint 4 B, F, G

This yields a “vote” tally as follows:

Matches to A B C D E F G # Hits 1 3 2 2 1 2 1

In this situation, it appears most probable that the fingerprintscorrespond to song B, since three of the four excerpt-fingerprintssupport such a conclusion. (Note that he excerpts—that which yieldedFingerprint 2—does not match song B at all.)

More sophisticated probabilistic techniques, of course, can be used.

Once a song has been identified in a database, a number of differentresponses can be triggered. One is to impose a set of usage controlscorresponding to terms set by the copyright holder (e.g., play controllimitations, record control, fee charges, etc.) Another is to identifymetadata related to the song, and provide the metadata to a user (or alink the metadata). In some such applications, the song is simplyidentified by title and artist, and this information is returned to theuser, e.g., by email, instant messaging, etc. With this information, theuser can be given an option to purchase the music in CD or electronicform, purchase related materials (t-shirts, concert tickets), etc. Agreat variety of other content-triggered actions are disclosed in thecited applications.

One of the advantages of fingerprint-based content identificationsystems is that not require any alteration to the content. Thus,recordings made 50 years ago can be fingerprinted, and identifiedthrough such techniques.

Going forward, there are various advantages to encoding the content withthe fingerprint. Thus, for example, a fingerprint identifier derivedfrom a song can be stored in a file header of a file containing thatsong. (MP3 files, MPEG files, and most other content file formatsinclude header fields in which such information can readily be stored.)The fingerprint can then obtained in two different ways—by reading theinfo, and by computation from the audio information. This redundancyoffers several advantages. One aids security. If a file has aheader-stored fingerprint that does not match a fingerprint derived fromthe file contents, something is amiss—the file may destructive (e.g., abomb or virus), or the file structure may misidentify the file contents.

In some embodiments, the fingerprint data (or watermark data) stored inthe header may be encrypted, and/or authenticated by a digital signaturesuch as a complete hash, or a few check bits or CRC bits. In such cases,the header data can be the primary source of the fingerprint (watermark)information, with the file contents being processed to re-derive thefingerprint (watermark) only if authentication of the fingerprint storedin the header fails. Instead of including the fingerprint in the header,the header can include an electronic address or pointer data indicatinganother location (e.g., a URL or database record) at which thefingerprint data is stored. Again, this information may be secured usingknown techniques.

Similarly, the fingerprint can point to a database that contains one ormore IDs that are added via a watermark. This is useful when CDs arebeing converted to MP3 files (i.e. ripped) and the fingerprint iscalculated from a hash of the table of contents (TOC) such as done withCDDB.com, or from all of the songs. In this case, the database entry forthat fingerprint could include a list of IDs for each song, and theseIDs are added via a watermark and/or frame header data. This can also beuseful where the content is identified based upon a group offingerprints from plural excerpts, in which case the database thatdetermines the content also contains an identifier, unrelated to thefingerprint(s) for that piece of content that can be embedded via awatermark.

Instead of, or in addition to, storing a fingerprint in a file header,the fingerprint data may be steganographically encoded into the filecontents itself, using known watermarking techniques (e.g., thosedisclosed in application Ser. No. 09/503,881, and U.S. Pat. Nos.6,061,793, 6,005,501 and 5,940,135). For example, the fingerprint ID canbe duplicated in the data embedded via a watermark.

In some arrangements, a watermark can convey a fingerprint, andauxiliary data as well. The file header can also convey the fingerprint,and the auxiliary data. And even if the file contents are separated fromthe header, and the watermark is corrupted or otherwise lost, thefingerprint can still be recovered from the content. In some cases, thelost auxiliary data can alternatively be obtained from information in adatabase record identified by the fingerprint (e.g., the auxiliaryinformation can be literally stored in the record, or the record canpoint to another source where the information is stored).

Instead of especially processing a content file for the purpose ofencoding fingerprint data, this action can be done automatically eachtime certain applications process the content for other purposes. Forexample, a rendering application (such as an MP3 player or MPEG viewer),a compression program, an operating system file management program, orother-purposed software, can calculate the fingerprint from the content,and encode the content with that information (e.g., using header data,or digital watermarking). It does this while the file is being processedfor another purpose, e.g., taking advantage of the file's copying into aprocessing system's RAM memory, from slower storage.

In formats in which content is segregated into portions, such as MP3frames, a fingerprint can be calculated for, and encoded in associationwith, each portion. Such fingerprints can later be crosschecked againstfingerprint data calculated from the content information, e.g., toconfirm delivery of paid-for content. Such fingerprints may be encryptedand locked to the content, as contemplated in application Ser. No.09/620,019.

In addition, in this frame based systems, the fingerprint data and/orwatermark data can be embedded with some or all data throughout eachframes. This way a streaming system can use the header to first checkthe song for identification, and if that identification is absent or notauthenticated, the system can check for the watermark and/or calculatethe fingerprint. This improves the efficiency and cost of the detectingsystem.

Before being encrypted and digitally signed, the data in the frameheader can be modified by the content, possibly a hash of the content ora few critical bits of content. Thus, the frame header data cannot betransferred between content. When reading the data, it must be modifiedby the inverse transform of the earlier modification. This system can beapplied whether the data is embedded throughout each frame or all in aglobal file header and is discussed in application Ser. No. 09/404,291entitled “Method And Apparatus For Robust Embedded Data” by Ken Levy onSep. 23, 1999. Reading this secure header data is only slightly morecomplex than without the modification, such that the system is moreefficient than always having to calculate the fingerprint and/or detectthe watermark.

COLLABORATION

In some situations, content may be processed by plural users, at aboutthe same time, to generate corresponding identifiers. This may occur,for example, where the content is a song or advertisement broadcast overthe radio. Many listeners in a metropolitan area may process audio fromthe same song broadcast over the radio, e.g., to learn the artist orsong title, to engage in some related e-commerce activity, or foranother purpose (such as the other purposes identified in the citedapplications).

In such cases it may be desirable to employ collaboration between suchusers, e.g., to assure more accurate results, to reduce the processingburden, etc.

In one embodiment, each user generates several different fingerprintsfrom the content (such as those identified in the table, above). Thesefingerprints may be aggregated with other fingerprints submitted fromother users within a given time window (e.g., within the past twentyseconds, or within the past fifteen and next five seconds). Since moredata is being considered, the “correct” match may more likely stand outfrom spurious, incorrect matches.

Consider Users 1 and 2, whose content yields fingerprints giving thefollowing matches (User 1 is unchanged from the earlier example):

Fingerprinted excerpt Matches these songs in database User 1,Fingerprint N A, B, C User 1, Fingerprint N + 1 C, D, E User 1,Fingerprint N + 2 B, D, F User 1, Fingerprint N + 3 B, F, G User 2,Fingerprint M A, B, E User 2, Fingerprint M + 1 H, I, A User 2,Fingerprint M + 2 X, Y, Z

Aggregating the fingerprints from the two users results in an enhancedvote tally in which song B is the evident correct choice—with a higherprobability of certainty than in the example earlier given involving asingle user:

Matches to A B C D E F G H I X Y Z # Hits 2 4 2 2 2 2 1 1 1 1 1 1

Moreover, note that User 2's results are wholly ambiguous—no songreceived more than a single candidate match. Only when augmented byconsideration of fingerprints from User 1 can a determination for User 2be made. This collaboration aids the situation where several users arelistening to the same content. If two users are listening to differentcontent, it is highly probable that the fingerprints of the two userswill be uncorrelated. No benefit arises in this situation, but thecollaboration does not work an impairment, either. (In identifying thesong for User 1, the system would only check the candidates for whomUser 1 voted. Thus, if the above table showed 5 votes for a song J, thatlarge vote count would not be considered in identifying the song forUser 1, since none of the fingerprints from User 1 corresponded to thatsong.)

It will be recognized that the different fingerprints obtained bydifferent users from the same song may be due to a myriad of differentfactors, such as ambient noise, radio multipath reception, differentstart times for audio capture, etc.

In the example just given, the number of fingerprints computed for eachuser can be reduced when compared with non-collaborative approaches,while still providing enhanced confidence in the final songdetermination.

Another collaborative embodiment employs a reference system. Consideragain the example of radio broadcasts in a metropolitan area. Referencereceivers can be installed that continuously receive audio from each ofseveral different radio stations. Instead of relying on sound picked upby a microphone from an ambient setting, the reference receivers cangenerate fingerprint data from the audio in electronic form (e.g., thefingerprint-generation system can be wired to the audio output of thereceiver). Without the distortion inherent in rendering through aloudspeaker, sensing through a microphone, and ambient noise effects,more accurate fingerprints may be obtained.

The reference fingerprints can be applied to the database to identify—inessentially real-time and with a high degree of certainty—the songs (orother audio signals) being broadcast by each station. The database caninclude a set of fingerprints associated with the song. Alternatively,the reference receiver can generate fingerprints corresponding to theidentified song.

Consumers listen to audio, and fingerprints are generated therefrom, asbefore. However, instead of applying the consumer-audio fingerprints tothe database (which may involve matching to one of hundreds of thousandsof possible songs), the consumer fingerprints are instead compared tothe fingerprints generated by the reference receivers (or songsdetermined there from). The number of such reference fingerprints willbe relatively low, related to the number of broadcast stations beingmonitored. If a consumer-audio fingerprint correlates well with one ofthe reference fingerprints, then the song corresponding to thatreference fingerprint is identified as the song to which the consumer islistening. If the consumer-audio fingerprint does not correlate wellwith any of the reference fingerprints, then the system may determinethat the audio heard by the consumer is not in the subset monitored bythe reference receivers, and the consumer-audio fingerprints canthereafter be processed against the full fingerprint database, asearlier described.

The system just described is well suited for applications in which thegeographical location of the consumer is known, or can be inferred. Forexample, if the consumer device that is listening to the audio is a cellphone, and the cellular wireless infrastructure is used to relay datawith the phone, the cell system can determine whether the geographicallocation of the listener (e.g., by area code, cell site, etc.). (Use ofsuch cell-system data to help geographically locate the user can beemployed advantageously in several such song-identification systems.).

Even if the consumer's location cannot be determined, the number ofsongs playing on radio stations nationwide is still a small subset ofthe total number of possible songs. So a nationwide system, withmonitoring stations in many metropolitan areas, can be used toadvantage.

As an optional enhancement to such a collaborative system, broadcastsignals (e.g., audio signals) are digitally watermarked. The digitalwatermark preferably contains plural-bit data, which is used to identifythe audio signal (e.g., a set of audio fingerprints from the audiosignal, song title, copyright, album, artist, and/or record label, etc.,etc.). The plural-bit data can either directly or indirectly identifythe audio signal. In the indirect case, the plural-bit data includes aunique identifier, which can be used to interrogate a database. Thedatabase preferably includes some or all of the identifying informationmentioned above. A reference receiver decodes an embedded digitalwatermark from a received audio signal. The unique identifier is used tointerrogate the database to identify a fingerprint or a set offingerprints associated with the particular audio signal. In some cases,the set includes one fingerprint; in other cases, the set includes aplurality of fingerprints. On the user side, fingerprints are generatedand relayed to the reference receiver (or associated interface). Theuser's fingerprints are then compared against the referencefingerprints, as discussed above in the earlier embodiments.

The foregoing are just exemplary implementations of the presentinvention. It will be recognized that there are a great number ofvariations on these basic themes. The foregoing illustrates but a fewapplications of the detailed technology. There are many others.

To provide a comprehensive disclosure without unduly lengthening thisspecification, applicants incorporate by reference the patents andpatent applications cited above. It is applicant's express intention toteach that the methods detailed herein are applicable in connection withthe technologies and applications detailed in these cited patents andapplications.

Although the foregoing specification has focused on audio applications,it will be recognized that the same principles are likewise applicablewith other forms of content, including still imagery, motion pictures,video, etc. References to “songs” are illustrative only, and are notintended to limit the present invention. The inventive methods andsystems could also be applied other audio, image, video signals as well.Also, for example, Digimarc MediaBridge linking from objects tocorresponding internet resources can be based on identifiers derivedfrom captured image data or the like, rather than from embeddedwatermarks. As such, the technique is applicable to images and video.

1. A method comprising: aggregating first fingerprint data and secondfingerprint data, wherein fingerprint data comprises at least areduced-bit representation of content, and wherein the first fingerprintdata originated at a first source and the second fingerprint dataoriginated at second source, and wherein the first source and the secondsource are remotely located; identifying information associated with thefirst fingerprint data and the second fingerprint data; and determininga subset of the associated information.
 2. The method according to claim1, wherein said determining is based at least in part on a frequencyoccurrence of the subset, and wherein the frequency occurrence comprisesa vote tally.
 3. The method according to claim 1, wherein saiddetermining is based at least in part on a frequency occurrence of thesubset, and wherein the subset comprises at least one of audio, video,or image data.
 4. The method according to claim 3, wherein theassociated information comprises at least one of audio, video or imagedata.
 5. The method of claim 1, wherein said aggregating comprisesaggregating fingerprint data within a predetermined time period.
 6. Themethod according to claim 1, wherein the first fingerprint datacomprises a first set of audio fingerprints, and wherein the secondfingerprint data comprises a second set of audio fingerprints.
 7. Amethod to match a song based on an audio fingerprint, said methodcomprising: aggregating a first set of audio fingerprints provided by afirst device with a second set of audio fingerprints provided by aremotely located second device; determining a plurality of songsrelating to the aggregated fingerprints; and selecting a song from theplurality of songs based on a number of times a selected song matchesthe aggregated fingerprints.
 8. The method according to claim 7, whereinthe selected song includes the highest number of matches.
 9. A methodcomprising: receiving a signal from a first broadcast source at areference receiver; generating first fingerprint data from the receivedsignal; applying the first fingerprint data to a database to selectassociated information; receiving second fingerprint data; and comparingthe second fingerprint data with the associated information.
 10. Themethod according to claim 9, wherein said comparing comprises selectinga subset from the associated information based on a vote tally.
 11. Amethod comprising: receiving a signal from a first broadcast source at areference receiver; generating first fingerprint data from the receivedsignal; applying the first fingerprint data to a database to selectassociated information; receiving second fingerprint data; and comparingthe second fingerprint data with the associated information, whereinsaid comparing comprises selecting a subset from the associatedinformation based on a vote tally, and wherein the vote tally includesprobabilities of a match with the second fingerprint data, and whereinthe selected subset has a highest probability of a match.
 12. A methodcomprising: receiving a signal from a first broadcast source at areference receiver; generating first fingerprint data from the receivedsignal; applying the first fingerprint data to a database to selectassociated information; receiving second fingerprint data; and comparingthe second fingerprint data with the associated information, wherein auser device generates the second fingerprint data.
 13. A methodcomprising: receiving a signal from a first broadcast source at areference receiver; generating first fingerprint data from the receivedsignal; applying the first fingerprint data to a database to selectassociated information; receiving second fingerprint data, wherein acell phone generates the second fingerprint data; and comparing thesecond fingerprint data with the associated information.
 14. A methodcomprising: receiving a signal from a first broadcast source at areference receiver; generating first fingerprint data from the receivedsignal; applying the first fingerprint data to a database to selectassociated information; receiving second fingerprint data, wherein auser device generates the second fingerprint data; comparing the secondfingerprint data with the associated information; and determining ageographical location of the user device.
 15. The method according toclaim 14, wherein the user device comprises a cell phone, and whereinthe geographical location of the user device is determined by at leastone of area code, cell site, device identifier, repeater identifier, oralpha-numeric data.
 16. A method comprising: receiving a signal from afirst broadcast source at a reference receiver; generating firstfingerprint data from the received signal; applying the firstfingerprint data to a database to select associated information;receiving second fingerprint data; comparing the second fingerprint datawith the associated information; receiving a signal from a secondbroadcast source at the reference receiver; generating third fingerprintdata from the received signal of the second broadcast source; andapplying the third fingerprint data to the database to select associatedinformation.
 17. The method according to claim 16, wherein the referencereceiver comprises a plurality of receivers.
 18. The method according toclaim 17, wherein at least a first receiver of the plurality ofreceivers and a second receiver of the plurality of receivers arelocated in different geographical locations.
 19. The method according toclaim 9, wherein when a comparison of the second fingerprint data withthe associated information does not identify a subset of the associateddata, said method further comprises querying a second database todetermine additional associated information.
 20. A method comprising:receiving a signal front a first broadcast source at a referencereceiver, the signal comprising an embedded digital watermark; decodingthe digital watermark to obtain a plural-bit identifier; interrogating adatabase with the identifier to identify a set of fingerprintsassociated with the received signal; receiving second fingerprint data;and comparing the second fingerprint data with the set of fingerprints.21. The method according to claim 20, wherein said comparing comprisesselecting a subset from the set of fingerprints based on a vote tally.22. A method comprising: cumulating a first set of representations ofaudio or video with a second set of representations of audio or video,wherein the representations comprise reduced-bit representations ofaudio or video, and wherein the first set of representations areprovided from a first device and the second set of representations areprovided from a second device; determining a plurality of audio andvideo content relating to the cumulated sets; and selecting a set ofaudio or video content from the plurality of audio or video contentbased on a number of times a selected set of audio and video contentcorresponds with the cumulated sets.
 23. A method comprising: receivingcontent, wherein the content comprises an embedded digital watermark;decoding the digital watermark to obtain a plural-bit identifier;deriving a reduced-bit representation of the content; accessing adatabase with at least the plural-bit identifier; and using at least thereduced-bit representation of the content to help identify orauthenticate the content.